Global financial crisis, ownership and bank profit ...Profit efficiency measures how close a bank is in producing the maximum level of profit, given the amount of inputs and outputs

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Global financial crisis, ownership and bank profitefficiency in the Bangladesh’s state owned and private

commercial banks

Crisis financiera global, propiedad y eficiencia de las ganancias en losbancos comerciales estatales y privados en Bangladesh

Fakarudin Kamarudin a,∗, Fadzlan Sufian b, Annuar Md. Nassir a

a Universiti Putra Malaysia, Serdang, Selangor, Malaysiab Taylor’s University, Subang Jaya, Selangor, Malaysia

Received 4 May 2015; accepted 5 February 2016Available online 12 August 2016

Abstract

This paper studies the impact of global financial crisis focusing on State Owned Commercial Banks (SCBs)and Private Commercial Banks (PCBs) ownership and others bank specific and macroeconomics factorsinfluencing profit efficiency level of the Bangladesh banking sector. The Slack-Based Data EnvelopmentAnalysis (SBM-DEA) method employed to compute the profit efficiency of 31 commercial banks operating inthe Bangladesh over the years 2004–2011. Furthermore, the multivariate panel regression analysis frameworkbased on the Ordinary Least Square (OLS) and Generalized Least Square (GLS) methods comprising theFixed Effect (FE) and Random Effect (RE) models adopted to examine the determinants of banks profitefficiency. Results indicate the levels of profit efficiency on SCBs and PCBs are increasing by 3.7% and5.8% during financial crisis years. However, over the period of post financial crisis years exhibited, profitefficiency levels on SCBs and PCBs are decreasing by 38.7% and 9.9%. Although profit efficiency levelson both ownership of banks show declining over the post financial crisis years, the PCBs still higher thanSCBs (67.8% > 60.1%) but insignificantly different. Furthermore, the findings reveals that the relationshipof size of bank, liquidity, economic growth and market concentration are significantly negative with profitefficiency of SCBs but positive to PCBs. Meanwhile, the factors of capitalization, credit risk and inflation

∗ Corresponding author.E-mail addresses: [email protected], [email protected] (F. Kamarudin).Peer Review under the responsibility of Universidad Nacional Autónoma de México.

http://dx.doi.org/10.1016/j.cya.2016.07.0060186-1042/All Rights Reserved © 2016 Universidad Nacional Autónoma de México, Facultad de Contaduría y Admin-istración. This is an open access item distributed under the Creative Commons CC License BY-NC-ND 4.0.

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706 F. Kamarudin et al. / Contaduría y Administración 61 (2016) 705–745

are significant with the positive and negative sign only to the profit efficiency of the SCBs over the periodof post global financial crisis.All Rights Reserved © 2016 Universidad Nacional Autónoma de México, Facultad de Contaduría y Admin-istración. This is an open access item distributed under the Creative Commons CC License BY-NC-ND4.0.

JEL classification: G21; G28Keywords: Global financial crisis; State Owned Commercial Banks; Private Commercial Banks; Profit efficiency

Resumen

Este estudio investiga el impacto de la crisis financiera global enfocándose en la propiedad de los bancoscomerciales del Estado (SCB) y privados (PCB) y otros factores específicos de la banca y macroeconómi-cos que influyen en el nivel de eficiencia de las ganancias del sector bancario de Bangladesh. Se empleóel método de análisis envolvente de datos basado en el déficit (Slack-Based Data Envelopment Analysis[SBM-DEA]) para calcular la eficiencia en las ganancias de los 31 bancos comerciales que operaron enBangladesh en los anos 2004 a 2011. Por otra parte, el marco de análisis de regresión multivariante del panelbasado en los métodos de mínimos cuadrados ordinarios (MCO) y de mínimos cuadrados generalizados(MCG) que comprenden los modelos de efecto fijo (EF) y de efecto aleatorio (EA) se adoptó para examinarlos determinantes de la eficiencia de las ganancias de los bancos. Los resultados indican que los niveles deeficiencia de las ganancias en los SCB y los PCB son crecientes en un 3.7 y un 5.8% durante los anos de crisisfinanciera. Sin embargo, durante el período de anos posteriores a la crisis financiera exhibida, los niveles deeficiencia en beneficios en los SCB y los PCB son decrecientes en un 38.7 y un 9.9%. Aunque los nivelesde eficiencia en las ganancias de los bancos de ambos tipos de propiedad muestran disminución a lo largo delos anos posteriores a la crisis financiera, en los PCB son más altos que en los SCB (67,8% > 60,1%), perono significativamente distintos. Además, los resultados revelan que las relaciones del tamano del banco, laliquidez, el crecimiento económico y la concentración del mercado son significativamente negativas conla eficiencia en las ganancias de los SCB pero positivas para los PCB. Mientras tanto, los factores de capi-talización, el riesgo de crédito y la inflación son significativos con el signo positivo y negativo solo para laeficiencia de las ganancias de los SCB durante el período de la post-crisis financiera global.Derechos Reservados © 2016 Universidad Nacional Autónoma de México, Facultad de Contaduría y Admin-istración. Este es un artículo de acceso abierto distribuido bajo los términos de la Licencia Creative CommonsCC BY-NC-ND 4.0.

Códigos JEL: G21; G28Palabras clave: Crisis financiera global; Bancos comerciales de propiedad estatal; Bancos comerciales privados; Eficienciade las ganancias

Introduction

The banking sector is the main source of funds for long-term investments and the foundation ofeconomic growth (Schumpeter, 1934). In most developing countries, the banking sector representsthe backbone of the financial system. Therefore, an efficient and profitable banking sector mayhelp ensure an effective financial system which is conducive to economic growth and development.Levine (1998) points out that the efficiency of financial intermediation affects a country’s economicgrowth and at the same time, bank (financial intermediation) insolvencies could result in systemiccrises and consequently negative implications on the economy.

F. Kamarudin et al. / Contaduría y Administración 61 (2016) 705–745 707

The banking sector is considered the backbone of most economies and plays a importantrole in attaining economic growth and development and becomes the most important mecha-nisms of Bangladesh financial system since the early 1970s. During the early years, all financialinstitutions, including commercial banks, are required to fulfil economic objectives set by the gov-ernment. However, the efficiency of the banking sector has become an imperative issue amongpolicymakers in Bangladesh since the formation of the National Commission on Money, Bank-ing and Credit in 1986 (Shameem, 1995). The purpose for the establishment of the commissionamong others is to find solutions for efficient operations and management of the banking system(Shameem, 1995). In maintaining the stability of the banking system, the efficiency of the bankingsector is important so as to ensure that banks remain profitable and healthy.

It would be reasonable to expect that improvements in profit efficiency could lead to higherbank profitability levels and help ensure the sustainability of the country’s economic growth.Besides, profit efficiency is also in line with firms’ main objective that is to maximize profitsince it takes into account both the cost and revenue effects on changes in outputs scale andscope. Profit efficiency measures how close a bank is in producing the maximum level of profit,given the amount of inputs and outputs and their price levels (Ariff & Can, 2008). Thus, profitefficiency provides a complete description on the economic goal of a bank which requires thatbanks reduce their costs and increase their revenues. Furthermore, Berger and Mester (2003)among others suggest that profit efficiency offers valuable information on the efficiency of bankmanagements.

Nevertheless, during the years 2007–2008 financial crisis has resulted in bank foreclo-sures in both the developed and developing economies. Throughout the period, approximately168 United States (U.S.) banks have failed, while the profitability and the efficiency lev-els of banking sectors worldwide declined abruptly. The financial crisis illustrated howruinous problems in the financial sector could be for the entire economy. In the case ofBangladesh country, the impact of this global financial crisis is slightly different to otheraffected countries mostly from European and U.S. Although the global financial crisis hasnot been directly felt because of the shielding of the economy from the most immediateeffects of the crisis, the economy of Bangladesh could be poorly effected due to the insta-bility of the financial market and economic conditions in the developed and several emergingeconomies.

In fact, even though it is hard to speculate how bad the financial crisis would affect the devel-oping countries, the failing financial institutions and toxic assets in US and others developedcountries can indirectly provide the lower level of profit efficiency to the developing countries’banks. Thus, the developing country like Bangladesh could use the phenomena of financial crisisthat affect the developed countries as a guidance to provide the wise strategies in constructingtheir financial systems to remain the higher level of bank profit efficiency. Furthermore, numer-ous developed and developing countries experienced some important ownership transformationsin several dimension during the years 2007 to 2013 due to the impact of global financial crisis(Claessens & Horen, 2014). This phenomena leads several banks retrenched from foreign activ-ities, others grasped opportunities to expand abroad or increase their market shares in foreigncountries to ensure the.

In fact, the factor of ownership can also significantly influence the efficiency of the banks.According to Isik and Hassan (2003) the banking sectors have heterogeneous ownership, cor-porate, market and risk characteristics. The selection of the ownership such as local, foreign,private, public, state, etc. is vital in the context of non-bank firms and banking sector (Boubakri,Cosset, Fischer, & Guedhami, 2005). Besides, the ownership represent an essential element for


the development of health banking sector in developing countries. Generally, the ownership ofBangladesh banks can be categorized into four groups namely Nationalized Commercial Banks orState Owned Commercial Banks (SCBs), Private Commercial Banks (PCBs), Specialized Devel-opment Banks (SDBs), and Foreign Commercial Banks (FCBs). The SCBs (consists of four bankswhich are Agrani Bank, Janata Bank, Rupali Bank Limited and Sonali banks) were formed via theevent of merger process of all commercial banks operating in Bangladesh except foreign banks.SCBs were considered as the proper means of generating savings that can be facilitate industrialfinance to the sectors of the economy with the greatest development prospects (Islam, Siddiqui,Hossain, & Karim, 2014).

The PCBs lead the Bangladesh banking sector since they cover more than 50% of total assetsand deposits. Basically, the PCBs’ performance is higher than SCBs and SDBs because theirquality and superb services such as banking service automation and client service innovation.Islam et al. (2014) reported that the PCBs have rapidly occupy the market share at the expense ofthe SCBs. In present, the PCBs have more than 59% of total deposits but SCBs have only 28%and PCBs assets coverage is 58% while it is only 29% in SCBs. Meanwhile, the SDBs and FCBsare not similar to both SCBs and PCBs commercial banks ownership. SDBs are formed specifi-cally to promote agricultural development and to promote small and medium entrepreneurship inBangladesh, whilst FCBs are the banks that operating in the Bangladesh which was incorporated inabroad.

Therefore, this study investigate for the first time empirical evidence on impact of global finan-cial crisis focusing on SCBs and PCBs ownership and others bank specific and macroeconomicsfactors influencing profit efficiency level of the Bangladesh banking sector. Although studies onbank efficiency are voluminous, these studies have mainly concentrated on the banking sectorsof the western and developed countries (Berger, 2007). Besides, very few have been examineissues of global financial crisis specifically on SCBs and PCBs ownership on the specific profitefficiency in the Bangladesh banks. On the other hand, empirical evidence on the developingcountries is relatively scarce and the majority of these studies focuses on the technical, pure tech-nical, and scale efficiency concepts. To do so, we adopt a two stage analysis. In the first stage,we employ the Slack-Based Data Envelopment Analysis (SBM-DEA) method to compute theprofit efficiency of 31 commercial banks operating in the Bangladesh banking sector during theperiod 2004–2011 which encapsulates the most recent global financial crisis period and coveredthe types of bank ownership. In the second stage, we employ a multivariate panel regressionanalysis framework based on the Ordinary Least Square (OLS) and Generalized Least Square(GLS) methods comprising the Fixed Effect (FE) and Random Effect (RE) models to examinethe potential determinants of banks’ profit efficiency.

The paper is set out as follows: the next section provides review of the related literature andhypotheses development, followed by “Data and methodology employed” section which outlinesthe data and methodology employed by the study. “Empirical results” section reports the empiricalfindings. Finally, we conclude in “Conclusions” section with some discussions on the policy issuesand offers avenues for future research.

Theoretical framework and literature review

Cobb Douglas production theory

The basic concept of efficiency is that it measures how well firms transform their inputs intooutputs according to their behavioural objectives (Fare, Grosskopf, Norris, & Zhang, 1994).


A firm is said to be efficient if it is able to achieve its goals and inefficient if it fails. Innormal circumstances, a firm’s goal is assumed to be cost minimization of production. Thus,any waste of inputs is to be avoided so that there is no idleness in the use of resources. Inthe production theory, it is often assumed that firms are behaving efficiently in an economic sense.The production theory originally proposed by Cobb and Douglas (1928) namely Cobb DouglassProduction Theory assumes that firms behave efficiently in an economic sense. He developsthe production theory from the movement of labour, capital, production, value and wages forthe manufacturing industries. According to Fare, Grosskopf, and Lovell (1985), firms are able tosuccessfully allocate all resources in an efficient manner relative to the constraints imposed by thestructure of the production technology, by the structure of input and output markets, and relativeto whatever behavioural goals attributed to the producers.

Furthermore, Berger and Humphrey (1997) extend the production function model to the bank-ing sector by focusing mainly on financial sector’s efficiency. The efficiency of the financial sectorunderlines the efficient allocations of financial resources that are required to promote productivity.This indicates that the economy has the opportunity to transfer the input of saving resources formore productive output such as investments.

Technical and cost efficiency

A wide range of models have been used to investigate a spectrum of efficiency related issuesin a wide range of environments. Koopmans (1951) was the first to provide the definition oftechnical efficiency where the producer is technically efficient if an increase in any output requiresa reduction in at least one output and if a reduction in any input requires an increase in atleast one other input or a reduction in at least an output. Liebstein (1966) on the other handwas the first to introduce the concept of X-efficiency. The X-efficiency concept defines costinefficiencies that are due to wasteful use of inputs, or managerial weakness. The X-efficiencyconcept seeks to explain why all firms do not succeed in minimizing the cost of productionand recognizes that the sources of X-efficiency may also be from outside of the firm. In thisregard, Button and Weyman-Jones (1992) suggest that X-inefficiency is due partly to the firm’sown actions as well as from exogenous factors surrounding the environment in which the firmoperates.

Ariff and Can (2008) state that, the cost efficiency means that a firm is able to minimize thecosts of inputs while producing the same amount of outputs sold at certain. Berger and Humphrey(1997) claimed that most of the previous studies focused on the cost efficiency and suggested thatresearch on the profit efficiency has been scarce. Most ignored the revenue and profit side on theefficiency of the banks since only nine out of 130 studies on efficiency of financial institutionsreviewed, had analyzed profit efficiency (Bader, Mohammed, Ariff, and Hassan, 2008). Basically,profit maximization requires a firm to choose an input and output bundle such that the outputbundle generates the maximum revenue possible from the corresponding input bundle. At thesame time, the input bundle chosen produces the corresponding output bundle at the lowestcost.

However, a study by Adongo, Strok, and Hasheela (2005) suggested that cost efficiency may notsufficient to describe the overall performance of the bank’s financial performance. The reason isthat cost efficiency only considers on how to minimize the cost, but it does not take into account therevenue gained from the provision of higher quality services. Also, cost efficiency only evaluatesthe performance holding output quantities statistically fixed at their observed levels, but it doesnot consider the optimally efficient levels involving a different scale and mix of outputs. Thus,


the bank is considered cost efficient at the current output which may or may not be cost efficientat optimal outputs. In this regard, the problem could be solved by examining the profit efficiencyconcept.

Profit efficiency

Profit efficiency refers to a firm’s maximization of profit and involves both the cost and revenueeffects on the changes in output scale and scope. Profit efficiency considers how successful a bankis in achieving maximum profit based on a given level of inputs and outputs and a level of theirprices (Ariff & Can, 2008). Therefore, the profit efficiency of a bank describes how it is ableto reduce cost and increase revenue. According to Berger and Mester (2003), profit efficiencyprovides more useful information on management efficiency. Also, Adongo et al. (2005) maintainthat there is profit efficiency if cost increase is due to increased or enhanced output but increasein revenue should exceed increase in cost.

Jayaraman and Srinivasan (2014) examine the profit efficiency of banks in India. Theyemployed the Nerlovian profit indicator to measure the banks profit efficiency. The profit ineffi-ciency of banks has been decomposed into technical and allocation inefficiency using directionaldistance function. The study suggest that banks profit inefficiency is due to inefficiency fromthe element of allocative and this indicates banks required to focus on optimal utilization ofinput–output mix.

Another study by Fu, Juo, Chiang, Yu, and Huang (2015) investigate the profit efficienciesof 70 Chinese and 34 Taiwanese banks in 2011. They include the equity capital as a quasi-fixedinput and develop the risk-based measure of the meta Nerlovian profit efficiency to consider riskconsideration of banks. The profit efficiency involve the basic two elements which are technologyand allocative efficiencies. The empirical results summarized that the Chinese joint-equity banks,Chinese state-owned banks and Taiwanese state-owned banks perform the best in meta profitefficiency.

In the Malaysian Islamic and conventional banks cases, Kamarudin and Yahya (2013) studythe cost, revenue and profit efficiency on both banks ownership. This research employed DataEnvelopment Analysis (DEA) method on the sample of 39 Islamic and conventional banks.They discovered that the levels of profit and cost efficiency for Islamic banks are lower thanconventional banks due to the factors of bank-specifics characteristics and macroeconomicconditions.

Kamarudin, Nordin, Muhammad, and Hamid (2014) examine the efficiency level on Islamicand conventional banks in Gulf Cooperation Council countries on the 74 banks over the years2007–2011. The findings seems to suggest that lower Islamic banks profit efficiency level due tothe higher level on banks revenue inefficiency. Another study by Sufian and Kamarudin (2015)also find the similar finding where the Islamic banks revenue efficiency has greater influence on theprofit efficiency levels in the selected Southeast Asian countries consists of Malaysia, Indonesiaand Brunei over the years 2006–2011. The results show that the level of profit efficiency in thedomestic Islamic banks is higher than foreign Islamic banks due to the higher level of revenueefficiency on domestic Islamic banks. They suggest that the higher profit efficiency levels thehigher profitability of the banks.

In general, most of the previous studies applied the Data Envelopment Analysis (DEA) tomeasure banks profit efficiency and its required the selection of inputs, input prices, outputs andoutput prices. The collection or selection of the bank inputs and outputs could be difficult in


the evaluation of the bank efficiency to be used in the first stage of DEA analysis. Bader et al.(2008) stated explicitly that there is ‘no perfect approach’ in the selection of the bank inputs andoutputs. Berger and Humphrey (1997) also found that there are some restrictions on the type ofvariables since there is a need for comparable data and to minimize possible biases due to differentaccounting practices in the collection of the variables. In fact, they stated that even in the samecountry, different banks might apply different accounting standards. The results of the efficiencyscores for each study on the bank efficiency will be affected due to the selection of variables.Thus, the DEA method requires bank inputs, input prices, outputs and output prices as the choiceis always an arbitrary issue (Ariff & Can, 2008; Berger & Humphrey, 1997; Sufian, Kamarudin,& Noor, 2013).

The above literature reveals the following research gaps. First, the majority of these stud-ies have mainly concentrated on other countries rather than Bangladesh. Second, empiricalevidence generally focus on the technical, cost, revenue and profit efficiencies in the bankingsectors without indentifying the impact of specific potential and macroeconomics determi-nants on banks profit efficiency. Finally, virtually nothing has been published on the specificimpact of global financial crisis and ownership to the specific profit efficiency concept in theBangladesh banks. In the light of these knowledge gaps, this paper seeks to provide newempirical evidence on the impact of global financial crisis to the profit efficiency and othersfactors that influence the level of profit efficiency of the SCBs and PCBs in Bangladesh bakingsector.

Data and methodology employed

The present study gathers data on commercial banks operating in the Bangladesh bankingsector during the years 2004 to 2011. The source of financial data is the Bureau van Dijk’sBankScope database which provides banks’ balance sheet and income statement information.Due to the entry and exit of banks during the years, the actual number of banks operating inthe Bangladesh banking sector varies. The final sample comprised of 31 commercial banks ofwhich complete data are available for the years 2004–2011. The analysis periods are dividedinto three event windows: 2004–2006, referred as pre global financial crisis, 2007–2008, referredas during global financial crisis, and 2009–2011, considered as post-merger period. This eventwindow was inspired by Rhoades (1998) who suggested that the three-year time period is optimalbecause about half of any efficiency gains should be realized within three years (−3,3). This factis almost unanimously agreed among the experts interviewed. In order to maintain homogeneity,only state owned commercial banks (SCBs) and private commercial banks (PCBs) are includedin the analysis. Foreign commercial banks (FCBs) and specialized development banks (SDBs)are excluded from the sample.

Data envelopment analysis (DEA)

The Data Envelopment Analysis (DEA) method is based on mathematical programming modeldeveloped by Charnes, Cooper, and Rhodes (1978) known as Charnes, Cooper and RhodesModel (CCR) that has been adopted by several studies to measures banks’ efficiency (e.g.Kamarudin, Nordin, & Nasir, 2013; Kamarudin, Nasir, Yahya, Said, & Nordin, 2014; Sufian,Muhammad, Nordin, Yahya, & Kamarudin, 2013; Sufian, Kamarudin, & Noor, 2014). The methodseeks to establish how the n decision making units (banks in our case) determine the envelopment


surface (the best practice efficiency frontier). The CCR model presupposes that there is no sig-nificant relationship between the scale of operations and efficiency by assuming constant returnsto scale (CRS) and is only justifiable when all decision making units are operating at an opti-mal scale. However, technological advances and regulatory changes may have different impactsacross banks of different sizes resulting in banks to face either economies or diseconomies ofscale (Assaf, Barros, & Matousek, 2011). To address this issue, Banker, Charnes, and Cooper(1984) extends the CCR model by relaxing the CRS assumption. The resulting Banker, Charnesand Cooper (BCC) Model is used to assess the efficiency of decision making units characterizedby variable returns to scale (VRS). Thus, the primary profit efficiency VRS model is given in Eq.(1):

maxs∑

r=1

qor yro −

m∑i=1

poi xio

subject ton∑

j=1

λjxij ≤ xio i = 1, 2, . . ., m;

n∑j=1

λjyrj ≥ yro r = 1, 2, . . ., s;

xio ≤ xio, yro ≥ yro

λj ≥ 0n∑

j=1

λj=1

(1)

where s is a is output observation, m is a input observation, r is a sth output, i is a mth input, qor

is unit price of the output r of decision making unitso (DMUo), poi is a unit price of the input i of

DMUo, yio is a rth output that maximize revenue for DMUo, xio is a ith input that minimize costfor DMUo, yro is a rth output for DMUo, xio is a ith input for DMUo, n is a DMU observation, jis a nth DMU, λj is a non-negative scalars, yrj is a sth output for nth DMU, xij is a mth input fornth DMU.

The slack-based data envelopment analysis (SBM-DEA)

The present study employs the non-parametric Slack-Based Data Envelopment Analysis(SBM-DEA) method to compute the efficiency of individual banks operating in the Bangladeshbanking sector (e.g. Sufian & Kamarudin, 2014). The method constructs the frontier of theobserved input-output ratios by linear programming techniques. The method is a non-radialefficiency measure dealing directly with input excesses and output shortfalls (Tone, 2002).A decision making units (DMU) that refer to bank is said to be efficient with a value ofunity if the DMU is on the frontier of the production possibility set with no input and output


slack. The estimated model is modified from Eq. (1) and the profit efficiency can be illustratedby

θ∗ = max θ

maxn∑

i=1

s−i +s∑

r=1

s+r

subject ton∑

j=1

λjxij + s−i = xio i = 1, 2, . . ., m;

n∑j=1

λjyrj − s+r = yro r = 1, 2, . . ., s;

λj, s−i , s+r ≥ 0

(2)

where DMU0 is one of the n DMUs under evaluation; xio and yro are the ith input and rth out-put for DMU0, respectively; and λj represents the unknown weights, where j represents thenumber of DMUs. The optimal value of θ* represents the distance from the efficient fron-tier. Therefore, the most efficient bank will have θ* = 1 and the inefficient bank will exhibitθ* < 1.

The SBM-DEA method is preferred to parametric estimation as the former deals with inputexcesses and output shortfalls simultaneously rather than holding the input or output at a givenlevel (Chan, Karim, Burton, & Aktan, 2014). Furthermore, Chiu and Chen (2009) suggest that theSBM-DEA method provides a well representation of banking operation in the real situation sincebanks are given a certain degree of control on both the input and output sides. For the purpose ofthis study, we adopt the SBM-DEA under the VRS model to solve the profit efficiency problem.Eq. (2) is modified to a VRS slack-based model as follows:

maxn∑

i=1

w−i s−i +

s∑r=1

w+r s+r

subject ton∑

j=1

λjxij + s−i = xio i = 1, 2, . . ., m;

n∑j=1

λjyrj − s+r = yro r = 1, 2, . . ., s;

λj, s−i , s+r ≥ 0n∑

j=1

λj = 1

(3)

where w−i and w+

r are user-specified weights obtained through value judgement. While, s−i isthe ith input slack and s+r is the rth output slack. The SBM-DEA method under the VRS model


assumes that production takes place with a disproportionate change in inputs and outputs. Thescalar, ρ, which captures the VRS based slack variables, is as follows:

ρ =(

1

a

m∑m=1

xoj,m − s−j,m

xoj,m

)(1

b

n∑n=1

yoj,n − s+j,n

yoj,n

)(4)

Approach selection, input and output variables

There are three main approaches that are widely used in the banking theory literature namely,production, intermediation, and value added approaches (Sealey & Lindley, 1977). The presentstudy adopts the intermediation approach attributed to three main reasons. First, the study attemptsto evaluate the efficiency of the whole banking sector and not branches of a particular bank. Second,the intermediation approach is the most preferred approach among researchers investigating theefficiency of banking sectors in developing countries (e.g. Bader et al., 2008; Isik & Hassan, 2002).Third, Sealey and Lindley (1977) suggest that financial institutions normally employ labour,physical capital, and deposits as their inputs to produce earning assets. Since the issue selectingapproaches is still arbitrary (Ariff & Can, 2008; Berger & Humphrey, 1997; Sufian & Habibullah,2009a; Sufian, Kamarudin, & Noor, 2013), this study had decided to use intermediation approachbecause we assume bank is more suitable to be classified as intermediary entity. Table 3 providesa listing of inputs and outputs chosen for a few of these studies. The choice of the inputs, outputs,input prices and output prices are guided by the choices made in previous studies summarized inTable 1a.

For the purpose of this study, three inputs and two outputs variables are chosen. The selectionof the input and output variables are based on Ariff and Can (2008) and other major studies on theefficiency of banking sectors in developing countries (e.g. Bader et al., 2008; Sufian, Kamarudin,& Noor, 2012; Sufian, Kamarudin, & Noor, 2013). The three input vector variables consist of x1:Deposits, x2: Labour, and x3: Capital. Meanwhile, the two output vector variables are y1: Loansand y2: Investments. The input prices consist of w1: Price of Deposit, w2: Price of Labour and w3:Price of Capital. The two output prices consist of r1: Price of Loans and r2: Price of Investment.The summary of data used to construct the efficiency frontiers are presented in Table 1b.

Multivariate panel regression analysis

To examine the relationship between the profit efficiency of Bangladesh banks and the con-textual variables, we use the Ordinary Least Square (OLS) and Generalized Least Square (GLS)methods comprising the Fixed Effect (FE) and Random Effect (RE) models to examine the poten-tial determinants of banks’ profit efficiency (Table 2). We estimate a linear regression model inthe following form

yit = αt + βjt(INTERjt + EXTERjt) + εjt (5)

where j refers to an individual bank; t refers to year; y refers to the profit efficiency and is theobservation of bank j in a particular year t; INTER represents the internal (bank specific) factors;EXTER represents the external (macroeconomic and market conditions) factors; εjt is a normallydistributed random variable disturbance term. By extending Eq. (5) to reflect the internal (bankspecific) and external (macroeconomic and market) variables discussed in “Theoretical framework


Table 1aSummary of inputs and outputs on bank efficiency analysis.

Study Inputs (x) Outputs (y) Input prices Output prices

Sufian, Kamarudin,and Noor (2013)

1. Deposits2: Labour

1. Loans2. Investment

1. Price of deposits (totalinterest expenses/deposits)2. Price of labour (personnelexpenses/total assets)

1. price of loans (interestincome on loansand others interestincome/loans)2. price ofinvestment(otheroperating income/income)

Sufian et al. (2012) 1. Deposit2. Labour3. Physical capital

1. Loans2. Investment3. Off-balancesheet item

1. Price of deposit(totalinterest expenses/deposits)2. Price of labour(personnelexpenses/total assets)3. Price of physicalcapital(Other operatingexpenses/fixed assets)

1. Price of loans(interestincome on loansand others interestincome/loan)2. Price ofinvestment(otheroperatingincome/investment)3. Price of off-balancesheet item(net fees andcommissions/off-balancesheet items)

Ariff and Can(2008)

1. Deposits2. No. ofemployees3. Physical capital

1. Loans2. Investment(short and longterm)

1. Price of deposits (interestpaid/deposits)2. Price of labour(personnelexpenses/no. of employees)3. Price of physical capital(other operatingexpenses/physical capital)

1. Price of loans(interestfrom loans/loans)2. Price ofinvestment(investmentincome/investment)

Bader et al. (2008) 1. Labour2. Fixed assets3. Total Funds

1. Total loans2. Investment3. Off-balancesheet items

1. Price of labour(totalpersonnel expenses/totalfunds)2. Price of fixedassets(depreciationexpenses/fixed assets)3. Price of funds(interestexpenses on deposits andnon-deposits funds plus otheroperating expenses/totalfunds)

1. Price of loans(interestincome/total loans)2. Price of invest-ment(investment/otherearning assets)3. Price of off balancesheet items(netcommission revenue plusnet earningincome/off-balance sheetitems)

and literature review” section, we estimate the following regression model:

LN(PE)it = α + βjt(LNTAjt + LNLLRGLjt + LNNIITAjt + LNETAjt

+ LNNIETAjt + LNLOANSTAjt + LNGDPt + LNINFLt

+ LNCR3t + PTCtLNTAjt ∗ PTCt + LNLLRGLjt ∗ PTCt

+ LNNIITAjt ∗ PTCt + LNETAjt ∗ PTCt + LNNIETAjt ∗ PTCt

+ LNLOANSTAjt ∗ PTCt + LNGDPt ∗ PTCt + LNINFLt ∗ PTCt

+ LNCR3t ∗ PTCt) + εjt (6)


Table 1bSummary statistics of the input and output variables 2004–2011.

Variable Crisis period Minimum Maximum Mean Std. deviation

Deposit (x1)Pre 4305.000 303,955.400 47,247.761 62,227.976During 5691.000 364,931.900 67,553.580 73,925.609Post 7545.700 535,288.400 108,993.191 94,721.342

Labour (x2)Pre 51.100 3718.700 655.133 801.734During 120.600 5822.300 1001.816 1148.399Post 142.400 9345.600 1690.263 1655.307

Capital (x3)Pre 17.300 3383.500 511.336 666.660During 37.200 9920.900 1364.423 2126.094Post 39.000 23,026.400 2892.831 3400.495

Loan (y1)Pre 3073.000 274,486.300 38,290.293 51,509.073During 5010.400 278,501.400 55,937.773 58,925.302Post 6256.200 345,991.300 87,373.341 66,540.986

Investment (y2)Pre 200.000 58,895.500 7079.030 10,910.332During 508.400 95,344.100 11,488.195 18,888.809Post 710.100 134,075.800 19,765.555 24,362.132

Price of deposit (w1)Pre 0.032 0.093 0.062 0.015During 0.033 0.111 0.070 0.019Post 0.034 0.173 0.066 0.019

Price of labour (w2)Pre 0.005 0.022 0.011 0.005During 0.005 0.023 0.012 0.005Post 0.005 0.023 0.013 0.004

Price of capital (w3)Pre 0.229 18.975 1.656 2.646During 0.180 12.857 1.318 1.870Post 0.075 2.522 0.807 0.537

Price of loan (r1)Pre 0.052 0.171 0.106 0.025During 0.047 0.169 0.117 0.024Post 0.095 0.247 0.128 0.022

Price of investment (r2)Pre 0.032 0.810 0.231 0.130During 0.000 0.628 0.119 0.110Post 0.000 0.285 0.047 0.044

Notes: x1: deposits (deposits and short term funding), x2: labour (personnel expenses), x3: capital (fixed assets), y1: loans(gross loan), y2: investment (total security)Pre: pre global financial crisis (2004–2006), During: during global financial crisis (2007–2008), Post: post financial crisis(2009–2011).Source: Bankscope Database & authors’ own calculation.

Empirical results

Before proceeding with the discussion of SBM-DEA results, this study first tested the ruleof thumb on the selection of inputs and outputs variables suggested by Cooper, Seiford, andTone (2002). Since the total number of DMUs (31 banks) in this study is more than the numbersof inputs and outputs variables (3 inputs × 2 outputs @ 3 [3 inputs + 2 outputs]), the selection ofvariables are valid since it complies with the rule of thumb and allows the efficiencies of DMUsto be measured. Next, by calculating the profit efficiency level on all pre, during and post globalfinancial crisis periods, we could observe the profit efficiency level of SCBs and PCBs ownershipto these periods and further obtain more robust results. Fig. 1 (graph) and Table 3 illustrates profitefficiency level pre, during and post global financial crisis on SCBs and PCBs ownership.


1.200

1.000

0.800

0.600

0.400

0.200

0.000Pro

fit e

ffici

ency

leve

lPre During Post

Global financial crisis

0.6780.601

0.777

0.9880.951

0.719

SCB PCB

Fig. 1. Graph on profit efficiency level pre, during and post global financial crisis on SCBs and PCBs ownership.

Table 2Variables used in the multivariate panel regression analysis.

Variable Description

DependentLN(PE) (Profit Efficiency) Natural log of the profit efficiency derived

from the DEA method.IndependentBank specific factors

LNTA (Size) The natural log of the accounting valueof bank j’s total assets in year t.

LNLLRGL (Credit risk) Natural log of loan loss reserves/gross loans.An indicator of credit risk, which shows howmuch a bank is provisioning in year t relativeto its total loans.

LNNIITA (Diversification) A measure of bank’s diversification towardsnon-interest income, computed as the naturallog of non-interest income over total assets.

LNETA (Capitalization) A measure of bank’s capital strength in yeart, calculated as the natural log of equity/totalassets.

LNNIETA (Overhead expenses) Calculated as the natural log of non-interestexpense/total assets and providesinformation on the efficiency of themanagement regarding expenses relativeto assets in year t.

LNLOANSTA (Liquidity) A measure of bank’s loans intensitycalculated as the natural log of total loansdivided by total assets.

Macroeconomic conditionsLNGDP (Economic growth) The natural log of gross domestic products.LNINFL (Inflation) The natural log of the rate of inflation.LNCR3 (Market concentration) The natural log of the three largest banks

asset concentration ratio.PTC (Dummy post global financial crisis) A binary variable that takes a value of

1 for the post global financial crisis period, 0otherwise.


Profit efficiency pre, during and post global financial crisis: SBCs vs. PCBs

Table 3 shows the means of profit efficiency for pre, during and post global financial crisisperiods of the SCBs and PCBs ownership. They are 95.1% vs. 71.9%, 98.8% vs. 77.7%, and60.1% vs. 67.8% for SCBs and PCBs ownership, respectively. As for pre global financial crisis,the average SCBs and PCBs generate 95.1% vs. 71.9%, of profit efficiency pre crisis, less thanwhat was initially expected to be generated. This result shows that the SCBs are generating moreprofit compared to the PCBs since the level of the profit efficiency for SCBs are higher than PCBs.With regards to profit efficiency during crisis period, the results indicate that, on average, the SCBsand PCBs shows the same findings since the level of profit efficiency of SCBs are higher thanPCBs. This indicate that the SCBs banks have earn 98.8% vs. 77.7%, respectively, of the profitfor the maximum outputs. For profit efficiency post global financial crisis, the results suggest thatthe average SCBs and PCBs can only earn 60.1% vs. 67.8% respectively, of what is available.Both categories of banks ownership lose the prospect of generating 39.9% and 32.2% additionaloutputs from the minimum level of inputs, respectively. Although SCBs and PCBs are show adeclining level of profit efficiency after the crisis period, the level of profit efficiency on PCBs arehigher than SCBs.

In conclusion, the empirical findings from this study indicate that although during the period ofglobal financial crisis increase the profit efficiency to SCBs and PCBs, the ultimate impact of thiscrisis reduce the profit efficiency level to both ownership since it can be observed over the postglobal financial crisis. Even though PCBs reported an improvement and higher profit efficiencythan SCBs, the efficiency level still lower compared to during financial crisis.

Robustness test

After examining the results derived from the SBM-DEA method, the issue of interest now iswhether the difference in the profit efficiency level pre, during and post global financial crisis ofthe SCBs and PCBs are statistically significant. The Mann–Whitney (Wilcoxon) is a relevant testfor two independent samples coming from populations having the same distribution. The mostrelevant reason is that the data violate the stringent assumptions of the independent group’s t-test. In what follows, we perform the non-parametric Mann–Whitney (Wilcoxon) test along witha series of other parametric (t-test) and non-parametric Kruskall–Wallis tests to obtain robustresults. Table 4 shows the robustness tests.

The results from the parametric t-test and non-parametric Mann–Whitney (Wilcoxon) testsuggest that the SCBs have exhibited a higher mean profit efficiency level than PCBs peerspre financial crisis (0.951 > 0.719) and significantly different at 1%. Likewise, the SCBs havealso exhibited a higher mean profit efficiency level during financial crisis compared to PCBs(0.988 < 0.777) and significantly different at 1%. The results from the parametric t-test are furtherconfirmed by the non-parametric Mann–Whitney (Wilcoxon) and Kruskall–Wallis tests, but onlyfor pre financial crisis. The interesting findings seem to suggests that the profit efficiency level ofPCBs are higher than SCBs after the period of financial crisis (0.678 > 0.601). However, the resulton the post global financial crisis shows that the profit efficiency level on PCBs and SCBs arenot significantly different and this indicate that they are behave homogenously during that period.Thus, we can conclude that in general the financial crisis significantly lead to the lower profitefficiency level on the Bangladesh banking sector and it can reduce the level of profit efficiencyspecifically on both ownership of banks (SCBs and PCBs).

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Table 3Profit efficiency level pre, during and post global financial crisis on SCBs and PCBs ownership.

No. SCB ownership Global financial crisis No. PCB ownership Global financial crisis

Pre During Post Pre During Post2004–2006 2007–2008 2009–2011 2004–2006 2007–2008 2009–2011

1 Agrani Bank 1.000 1.000 0.667 1 Arab Bangladesh Bank – – 0.3762 Janata Bank – – 1.000 2 Bangladesh Commerce Bank 1.000 1.000 1.0003 Rupali Bank 0.854 0.964 0.239 3 Bank Asia 1.000 1.000 0.6254 Shahjalal Bank 1.000 1.000 0.499 4 BRAC Bank – – 0.366

5 City Bank – – 0.7076 Dhaka Bank 0.566 1.000 0.1997 Dutch-Bangla Bank 0.270 0.045 0.5368 Eastern Bank – – 0.2499 Export Import Bank of Bangladesh – – 1.000

10 First Security Bank 0.777 1.000 1.00011 IFIC Bank 0.729 1.000 0.30912 Islami Bank Bangladesh 0.797 1.000 1.00013 Jamuna Bank 0.025 1.000 0.23014 Mercantile Bank 0.679 0.212 0.41915 Mutual Trust Bank – – 1.00016 National Bank 0.435 0.118 0.87617 National Credit and Commerce Bank 0.669 1.000 1.00018 One Bank 0.691 0.289 0.48719 Premier Bank – – 0.54420 Prime Bank 1.000 1.000 1.00021 Pubali Bank 0.492 0.199 0.43122 Sonali Bank 1.000 1.000 1.00023 Southeast Bank – – 1.00024 Standard Bank 1.000 1.000 1.00025 Trust Bank 1.000 0.127 0.67126 United Commercial Bank – – 0.46527 Uttara Bank 1.000 1.000 0.680

All mean 0.951 0.988 0.601 All mean 0.719 0.777 0.678


Table 4Summary of parametric and non-parametric tests.

Test groups

Parametric test Non-parametric test

Individual tests t-Test Mann–Whitney Kruskall–Wallis[Wilcoxon rank-sum] test Equality of populations test

Hypothesis Median SCBs = Median PCBs

Profit efficiency t(Prb > t) z(Prb > z) X2 (Prb > X2)

Test statistics Mean t Mean Rank z Mean Rank X2

Pre-crisisSCBs 0.951 3.126*** 39.222 −1.751* 39.222 3.066*

PCBs 0.719 29.577 29.577

During crisisSCBs 0.988 3.759*** 27.750 −1.250 27.750 1.563PCBs 0.777 21.671 21.671

Post-crisisSCBs 0.601 −0.695 41.167 −0.726 41.167 0.526PCBs 0.678 46.734 46.734

*** Correlation is significant at the 0.01 level (2-tailed).* Correlation is significant at the 0.01 level (2-tailed).

0

5

10

15

20

25

30

35

–2.0 –1.5 –1.0 –0.5 0.0 0.5

Series: Standardized ResidualsSample 2004 2011Observations 169

8.17e–160.1923720.648350

–1.9818230.530631

–1.7900086.086847

157.34700.000000

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis

Jarque-BeraProbability

Fig. 2. Normality test statistics on SCBs.

Residual analysis

Result of normality testFigs. 2 and 3 exhibits the group normality test statistics. The results on PCBs (Fig. 2) revealed

that the group value of skewness is -1.790, which indicates that the data are normal (error variable)since the value is in the range of ±1.96. Nevertheless, the values of Kurtosis and Jarque–Bera arenot normally distributed because the Kurtosis’s value is not in the range of ±2 and the value ofJarque–Bera is statistically significant at 1% level. While the results on SCBs shows that (Fig. 3)although the probability of Jarque–Bera is insignificant and the skewness value is around ±1.96,the kurtosis (3.977) is not around ±2. Therefore both results indicate that the error variable is not


0

2

4

6

8

10

–0. 15 –0.10 –0.05 0.00 0.05 0.10 0.15 0.20

Series: Standardized ResidualsSample 20 04 2011Observations 27

4.44e–15Mean 0.000485Median 0.182968Maximum

– 0.148027Minimum 0.075434Std. Dev. 0.590899Skewness 3.977162Kurtosis

2.645428Jarque-Bera 0.266411Probability

Fig. 3. Normality test statistics on PCBs.

normally distributed. Thus, adopting the GLS method in both SCBs and PCBs is more suitableand is expected to produce better results (Gujarati, 2002).

Result of heteroscedasticity testTables 5 and 6 exhibits the results of the White General Heteroscedasticity test. The results

of F-test for all SCBs and PCBs models fail to reject the null hypothesis of no heteroscedasticityproblem, suggesting that the error variance is constant by applying the GLS regression alongwith the White’s Heteroscedasticity Consistent Standard Errors technique, the heteroscedasticityproblem in this study was solved (Gujarati, 2002).

Result of autocorrelations testThis study used the Durbin-Watson (DW) to test the autocorrelation problems. Tables 7 and 8

show the results of the autocorrelation test on SCBs and PCBs which suggest that the entireproposed model has no serial correlation or the errors are independent of each others because thevalue of the DW statistic is around 2 (Gujarati, 2002).

Determinants of profit efficiency on SCBs and PCBs

In essence, results from the first stage identify the level of profit efficiency of the SCBs andPCBs for specific year and bank. In what follows, we proceed to identify the internal (bankspecific) and external (macroeconomic) factors which could improve the profit efficiency ofthe Bangladesh banking sector (Tables 9 and 10). To do so, we estimate 12 multivariate panelregression models which are presented in Tables 9 and 10. In Model 1, we report the regressionresults for the baseline regression model which include all six bank specific variables namelyLNTA, LNLLRGL, LNETA, LNNIITA, LNNIETA, and LNLOANSTA. In regression Model 2,we introduce the macroeconomic variables namely LNGDP, LNINFL, and LNCR3, while thebank specific variables are retained in the regression model. In regression Model 3, we includethe PTC variable to control for the post global financial crisis period.

Models 4–12 represent focused models adopted to identify the potential determinantsof SCBs’ and PCBs’ profit efficiency specifically on post global financial crisis period.All the bank specific and macroeconomic variables are retained in these models (Model4–Model 12). This study include interaction variables namely LNTA*PTC, LNLLRGL*PTC,

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Table 5White general heteroscedasticity test on SCBs.

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Model 10 Model 11 Model 12

F-statistic 1.322 0.346 1.040 1.836 0.544 0.280 0.403 0.321 0.141 0.943 1.651 2.315Obs R2 1.334 0.354 1.053 1.840 0.554 0.287 0.412 0.328 0.144 0.956 1.659 2.306Prob. Chi-Sq. χ2 0.248 0.552 0.305 0.175 0.457 0.592 0.521 0.567 0.704 0.328 0.198 0.129Ho (null-no het-

eroscedasticityproblem)

Fail to reject Ho

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Table 6White general heteroscedasticity test on PCBs.


F-statistic 0.744 0.794 0.828 0.755 0.836 0.666 0.820 0.788 0.805 0.829 0.855 0.827Obs R2 16.235 38.041 51.375 49.945 54.549 46.487 53.817 52.382 52.214 51.421 52.507 51.338Prob. Chi-Sq. χ2 0.756 0.759 0.718 0.819 0.707 0.915 0.731 0.776 0.753 0.717 0.679 0.720Ho (null-no het-

eroscedasticityproblem)

Fail to reject Ho

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Table 7Autocorrelation test using Durbin–Watson test on SCBs.


Durbin–Watsonstat

2.257 2.007 2.004 1.801 2.042 1.991 1.949 1.995 2.027 2.000 2.166 2.004

Sel Est method FE FE RE RE FE FE FE FE RE RE FE RE

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Table 8Autocorrelation test using Durbin–Watson test on PCBs.


Durbin–Watsonstat

1.956 1.920 1.927 2.317 1.934 2.274 2.299 1.936 1.932 1.927 1.925 1.927

Sel Est method RE RE RE FE RE FE FE RE RE RE RE RE

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Table 9Multivariate regression analysis on SCBs under ordinary least square model.

Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Model 10 Model 11 Model 12

C −7.816*** 8.998*** −8.09 −3.006 6.662 4.74 8.252*** 9.282*** −9.508* −7.81 −1.341 −8.747(2.618) (3.255) (5.480) (6.345) (4.291) (5.265) (3.027) (3.240) (5.660) (5.468) (5.146) (5.605)

Bank-specific variablesLNTA 0.361*** 0.638*** 0.835*** 0.842*** 0.647*** 0.633*** 0.671*** 0.639*** 0.842*** 0.833*** 0.790*** 0.834***

(0.127) (0.093) (0.071) (0.105) (0.052) (0.043) (0.064) (0.048) (0.072) (0.071) (0.075) (0.069)LNLLRGL 0.012 0.075 −0.053 −0.006 0.079 0.034 0.068 0.076 −0.049 −0.051 −0.011 −0.049

(0.062) (0.052) (0.077) (0.078) (0.049) (0.068) (0.055) (0.053) (0.075) (0.077) (0.077) (0.074)LNNIITA −0.556*** −0.640*** −0.714*** −0.758*** −0.621*** −0.542*** −0.661*** −0.656*** −0.733*** −0.713*** −0.693*** −0.717***

(0.113) (0.130) (0.046) (0.064) (0.075) (0.046) (0.096) (0.068) (0.042) (0.047) (0.050) (0.045)LNETA 0.077 0.674*** 0.741*** 0.772*** 0.673*** 0.614*** 0.719*** 0.666*** 0.727*** 0.740*** 0.716*** 0.738***

(0.053) (0.129) (0.020) (0.024) (0.059) (0.066) (0.032) (0.036) (0.021) (0.021) (0.029) (0.021)LNNIETA 0.237*** 0.059 0.292*** 0.246*** 0.129*** 0.116* 0.084** 0.044 0.296*** 0.288*** 0.202*** 0.294***

(0.065) (0.060) (0.062) (0.072) (0.048) (0.065) (0.040) (0.054) (0.061) (0.062) (0.054) (0.063)LNLOANSTA 3.278*** 2.158*** 3.043*** 2.908*** 1.765*** 2.271*** 2.273*** 2.220*** 3.490*** 3.037*** 2.914*** 3.106***

(1.093) (0.740) (0.348) (0.524) (0.505) (0.527) (0.544) (0.467) (0.393) (0.350) (0.436) (0.364)

Macroeconomic variablesLNGDP −5.385*** 0.655 −1.203 −4.269*** −3.973*** −5.067*** −5.584*** 0.736 0.577 −1.576 0.613

(0.916) (1.757) (1.905) (0.989) (1.268) (0.535) (0.666) (1.715) (1.753) (1.554) (1.737)LNINFL 0.896*** −1.465** −0.808 0.410** 0.578*** 0.705*** 0.979*** −1.406** −1.411** −0.168 −1.508**

(0.128) (0.594) (0.632) (0.196) (0.166) (0.073) (0.138) (0.559) (0.584) (0.324) (0.602)LNCR3 1.008*** −1.576*** −0.996 0.732* 0.684* 0.668 1.146** −1.430*** −1.599*** −1.405** −1.139***

(0.366) (0.505) (0.617) (0.416) (0.378) (0.513) (0.498) (0.442) (0.514) (0.583) (0.402)PTC −0.961***

(0.242)

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Table 9 (Continued)


Bank-specific on post global financial crisis variablesLNTA*PTC −0.137***

(0.050)LNLLRGL*PTC −0.268***

(0.092)LNNIITA*PTC −0.337

(0.221)LNETA*PTC −0.121

(0.087)LNNIETA*PTC 0.091

(0.156)LNLOANSTA*PTC −0.523***

(0.128)

Macroeconomic on post global financial crisis variablesLNGDP*PTC −0.269***

(0.068)LNINFL*PTC −0.777***

(0.244)LNCR3*PTC −0.581***

(0.146)

R2 0.292 0.585 0.776 0.709 0.638 0.621 0.59 0.587 0.777 0.774 0.715 0.777Adj R2 0.235 0.533 0.744 0.667 0.586 0.567 0.531 0.528 0.746 0.742 0.674 0.745F-statistic 5.095*** 11.143*** 24.301*** 17.017*** 12.327*** 11.479*** 10.068*** 9.949*** 24.436*** 24.005*** 17.578*** 24.333***

Durbin–Watson stat 1.544 1.901 2.011 1.82 1.673 1.873 1.951 1.914 2.048 2.008 1.938 2.000No. of Obs. 27 27 27 27 27 27 27 27 27 27 27 27

Figure in parentheses () are standard error.* Significance at the 10% level.

** Significance at the 5% level.*** Significance at the 1% level.

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Table 10Multivariate regression analysis on PCBs under ordinary least square model.


C −1.016 −3.324 0.815 0.336 −3.443 −2.768 −0.485 −4.307* 1.056 0.756 −0.816 0.864(0.677) (2.613) (1.146) (1.040) (2.569) (1.795) (1.136) (2.280) (1.166) (1.139) (1.162) (1.219)

Bank-specific variablesLNTA 0.084*** 0.118** 0.107** 0.076 0.114** 0.116** 0.108** 0.119** 0.110** 0.107** 0.109** 0.107**

(0.027) (0.049) (0.050) (0.057) (0.053) (0.052) (0.051) (0.048) (0.049) (0.050) (0.050) (0.050)LNLLRGL 0.016 0.053 0.109 0.114 0.042 0.064 0.081 0.037 0.118 0.109 0.099 0.107

(0.094) (0.076) (0.091) (0.093) (0.083) (0.081) (0.079) (0.079) (0.092) (0.091) (0.093) (0.091)LNNIITA −0.344*** −0.406*** −0.454*** −0.439*** −0.416*** −0.521** −0.506*** −0.354*** −0.448*** −0.454*** −0.454*** −0.453***

(0.110) (0.118) (0.124) (0.120) (0.119) (0.220) (0.146) (0.124) (0.123) (0.125) (0.129) (0.125)LNETA 0.175*** 0.261* 0.268** 0.239* 0.241 0.238 0.182 0.268* 0.291** 0.268** 0.267** 0.268**

(0.052) (0.138) (0.124) (0.124) (0.148) (0.156) (0.143) (0.141) (0.124) (0.124) (0.127) (0.124)LNNIETA −0.360 −0.266 −0.202 −0.189 −0.229 −0.242 −0.200 −0.245 −0.211 −0.202 −0.206 −0.206

(0.224) (0.232) (0.263) (0.264) (0.233) (0.232) (0.246) (0.185) (0.268) (0.263) (0.256) (0.262)LNLOANSTA 0.322 0.340 0.567 0.626 0.395 0.431 0.524 0.238 0.473 0.566 0.511 0.555

(0.357) (0.321) (0.392) (0.402) (0.343) (0.341) (0.375) (0.344) (0.363) (0.392) (0.383) (0.391)

Macroeconomic variablesLNGDP 0.117 −2.136*** −1.980*** 0.069 −0.232 −1.564** 0.686 −2.177*** −2.121*** −1.499** −2.041***

(0.722) (0.673) (0.689) (0.714) (0.414) (0.603) (0.632) (0.653) (0.675) (0.667) (0.677)LNINFL 0.282*** 1.288*** 1.250*** 0.339** 0.449** 1.061*** 0.022 1.304*** 1.272*** 0.809*** 1.280***

(0.069) (0.362) (0.394) (0.145) (0.226) (0.358) (0.226) (0.357) (0.360) (0.283) (0.381)LNCR3 0.832*** 2.235*** 2.250*** 0.927*** 1.063** 2.052*** 0.478 2.249*** 2.249*** 2.211** 2.025***

(0.254) (0.702) (0.788) (0.292) (0.457) (0.753) (0.479) (0.700) (0.712) (0.890) (0.653)PTC 0.408**

(0.159)

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Table 10 (Continued)


Bank-specific on post global financial crisis variablesLNTA*PTC 0.079**

(0.035)LNLLRGL*PTC 0.055

(0.107)LNNIITA*PTC 0.184

(0.225)LNETA*PTC 0.372***

(0.181)LNNIETA*PTC −0.240

(0.206)LNLOANSTA*PTC 0.221***

(0.084)

Macroeconomic on post global financial crisis variablesLNGDP*PTC 0.115**

(0.045)LNINFL*PTC 0.374*

(0.199)LNCR3*PTC 0.241**

(0.099)


Durbin–Watson stat 1.679 1.678 1.706 1.708 1.678 1.674 1.706 1.668 1.717 1.705 1.702 1.707No. of Obs. 169 169 169 169 169 169 169 169 169 169 169 169



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Table 11Panel regression analysis on SCBs under fixed and random effects model.

Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

FE RE FE RE FE RE FE RE FE RE FE RE

C −4.617*** −7.910** 3.929** 2.961 −7.843* −7.146 −5.735 −4.130 −0.068 3.036 4.201 −0.925(1.426) (3.046) (1.563) (4.072) (4.206) (5.293) (4.052) (5.953) (1.124) (4.721) (2.849) (5.924)

Bank-specific variablesLNTA −0.250** 0.357*** 0.099** 0.700*** 0.269*** 0.790*** 0.187*** 0.830*** −0.110** 0.663*** 0.100* 0.690***

(0.105) (0.144) (0.054) (0.050) (0.070) (0.081) (0.050) (0.109) (0.049) (0.058) (0.054) (0.047)LNLLRGL −0.055 −0.117 −0.031 −0.066 −0.087 −0.125 −0.083 −0.105 −0.023 0.010 −0.028 −0.101

(0.049) (0.064) (0.031) (0.109) (0.061) (0.097) (0.060) (0.102) (0.034) (0.062) (0.026) (0.117)LNNIITA −1.046*** −0.505* −0.784*** −0.563*** −0.742*** −0.567*** −0.795*** −0.617*** −0.755*** −0.530*** −0.814*** −0.452***

(0.211) (0.262) (0.199) (0.109) (0.146) (0.112) (0.171) (0.127) (0.199) (0.118) (0.123) (0.107)LNETA 0.278*** 0.085 0.290*** 0.563*** 0.591*** 0.699*** 0.562*** 0.729*** 0.295*** 0.602*** 0.285*** 0.539***

(0.093) (0.136) (0.037) (0.086) (0.057) (0.040) (0.058) (0.045) (0.052) (0.083) (0.051) (0.076)LNNIETA 0.016 0.319*** −0.086** 0.174** 0.206** 0.354*** 0.162** 0.326*** 0.038 0.192*** −0.095 0.235**

(0.055) (0.096) (0.042) (0.084) (0.089) (0.082) (0.089) (0.093) (0.027) (0.050) (0.061) (0.101)LNLOANSTA 3.279*** 3.321** 0.753 2.531*** 2.564*** 2.476*** 2.210*** 2.448*** −0.011 1.680*** 0.723 2.505***

(0.783) (1.314) (0.616) (0.586) (0.420) (0.427) (0.438) (0.526) (0.728) (0.583) (0.842) (0.542)

Macroeconomic variablesLNGDP −2.337*** −4.285*** 1.368 0.527 0.848 −0.715 −0.089 −3.391*** −2.434*** −2.836*

(0.208) (0.786) (1.313) (1.853) (1.269) (1.991) (0.329) (1.127) (0.624) (1.555)LNINFL 1.056*** 0.900*** −0.957** −1.462** −0.569 −1.000 0.360*** 0.217 1.107*** 0.396

(0.074) (0.152) (0.563) (0.659) (0.490) (0.706) (0.108) (0.209) (0.145) (0.252)LNCR3 0.897*** 1.768*** −1.234** −1.135 −0.980** −0.699 0.408 1.238*** 0.944** 1.405***

(0.315) (0.403) (0.499) (0.844) (0.480) (0.910) (0.464) (0.416) (0.435) (0.420)PTC −0.790*** −0.937***

(0.234) (0.289)

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Bank-specific on post global financial crisis variablesLNTA*PTC −0.128*** −0.150**

(0.042) (0.060)LNLLRGL*PTC −0.455*** −0.361***

(0.069) (0.104)LNNIITA*PTC 0.042 −0.460

(0.162) (0.328)LNETA*PTCLNNIETA*PTCLNLOANSTA*PTC

Macroeconomic on post global financial crisis variablesLNGDP*PTCLNINFL*PTCLNCR3*PTC


Durbin–Watson stat 2.257 1.412 2.007 1.665 2.276 2.004 2.202 1.801 2.042 1.521 1.991 1.756BP & LM x2 5.030** 4.560** 6.090** 5.370** 1.720** 3.460**

Hausman x2 25.910*** 32.950*** 10.19 2.58 50.150*** 52.040***

Sel Est method FE FE RE RE FE FENo. of Obs. 27 27 27 27 27 27 27 27 27 27 27 27

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C 6.047*** 2.601 3.939 2.864 −8.206* −8.775 −7.583* −6.959 −2.047 −2.711 −8.643* −7.632(1.415) (3.878) (2.485) (4.690) (4.352) (5.617) (4.166) (5.263) (3.193) (4.609) (4.442) (5.470)

Bank-specific variablesLNTA −0.111*** 0.727*** 0.105* 0.704*** 0.298*** 0.804*** 0.266*** 0.789*** 0.197*** 0.776*** 0.260*** 0.788***

(0.039) (0.064) (0.046) (0.076) (0.079) (0.083) (0.069) (0.081) (0.054) (0.082) (0.069) (0.080)LNLLRGL −0.041 −0.075 −0.031 −0.067 −0.077 −0.127 −0.086 −0.125 −0.059 −0.117 −0.084 −0.122

(0.032) (0.109) (0.031) (0.116) (0.058) (0.096) (0.061) (0.097) (0.054) (0.103) (0.059) (0.095)LNNIITA −0.735*** −0.584*** −0.796*** −0.560*** −0.764*** −0.587*** −0.742*** −0.566*** −0.741*** −0.545*** −0.742*** −0.568***

(0.198) (0.119) (0.202) (0.112) (0.142) (0.112) (0.148) (0.112) (0.176) (0.113) (0.146) (0.113)LNETA −0.062 0.604*** 0.286*** 0.567*** 0.566*** 0.680*** 0.587*** 0.697*** 0.493*** 0.660*** 0.603*** 0.698***

(0.070) (0.086) (0.058) (0.067) (0.050) (0.040) (0.056) (0.040) (0.046) (0.041) (0.061) (0.040)LNNIETA −0.206*** 0.190** −0.085 0.179 0.196** 0.361*** 0.202 0.351*** 0.087* 0.282*** 0.214 0.356***

(0.041) (0.083) (0.062) (0.111) (0.087) (0.081) (0.089) (0.081) (0.073) (0.078) (0.092) (0.083)LNLOANSTA −0.923 2.564*** 0.721 2.510*** 2.805*** 2.965*** 2.542*** 2.474*** 2.072*** 2.478*** 2.741*** 2.506***

(0.715) (0.565) (0.690) (0.519) (0.483) (0.502) (0.420) (0.427) (0.425) (0.459) (0.464) (0.440)Macroeconomic variablesLNGDP −2.441*** −4.111*** −2.346*** −4.216*** 1.163 0.655 1.3 0.476 −0.412 −1.050 1.385 0.465

(0.297) (0.755) (0.624) (1.239) (1.263) (1.840) (1.302) (1.846) (0.934) (1.610) (1.319) (1.849)LNINFL 1.762*** 0.780*** 1.073*** 0.864*** −0.809 −1.434*** −0.904 −1.417*** 0.226 −0.297 −1.037** −1.505***

(0.182) (0.163) (0.115) (0.264) (0.522) (0.633) (0.550) (0.648) (0.242) (0.355) (0.592) (0.680)LNCR3 1.993*** 1.567*** 0.920** 1.714*** −1.012*** −1.009 −1.245** −1.162 −0.866** −1.093 −0.919** −0.723

(0.484) (0.499) (0.420) (0.453) (0.434) (0.798) (0.503) (0.855) (0.421) (0.956) (0.411) (0.738)

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PTCBank-specific on post global financial crisis variablesLNTA*PTCLNLLRGL*PTCLNNIITA*PTCLNETA*PTC 0.393*** −0.083

(0.075) (0.063)LNNIETA*PTC 0.008 −0.039

(0.154) (0.294)LNLOANSTA*PTC −0.408*** −0.511***

(0.121) (0.156)Macroeconomic on post global financial crisis variablesLNGDP*PTC −0.220*** −0.263***

(0.065) (0.081)LNINFL*PTC −0.564*** −0.840***

(0.194) (0.303)LNCR3*PTC −0.485*** −0.568***

(0.144) (0.177)


Durbin–Watson stat 1.949 1.705 1.995 1.662 2.254 2.027 2.271 2.000 2.166 1.905 2.28 2.004BP & LM x2 4.270** 3.060** 4.790** 4.560** 4.500** 4.590**

Hausman x2 24.730*** 32.540*** 8.95 11.66 25.330** 11.68Sel Est method FE FE RE RE FE RENo. of Obs. 27 27 27 27 27 27 27 27 27 27 27 27



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Table 12Panel regression analysis on PCBs under fixed and random effects model.



C −1.300*** −2.210** −4.554** −5.002 −2.614 −0.729 −2.419 −1.666 −4.491** −5.076 −4.673** −4.249(0.425) (0.855) (1.875) (5.407) (2.087) (5.579) (2.118) (5.533) (1.979) (5.236) (1.840) (4.813)

Bank-specific variablesLNTA −0.034*** 0.087 −0.093 0.222*** −0.136 0.211*** −0.124 0.190*** −0.090 0.213*** −0.095 0.220***

(0.005) (0.099) (0.130) (0.065) (0.173) (0.065) (0.182) (0.062) (0.133) (0.063) (0.146) (0.067)LNLLRGL −0.003 0.008 −0.055 0.023 −0.027 0.041 −0.018 0.053 −0.056* −0.026 −0.057 0.035

(0.022) (0.055) (0.035) (0.068) (0.034) (0.074) (0.040) (0.073) (0.030) (0.067) (0.036) (0.066)LNNIITA −0.024 −0.268 −0.077 −0.338 −0.131 −0.397* −0.135 −0.400* −0.079 −0.326 −0.068 −0.426

(0.034) (0.195) (0.086) (0.229) (0.145) (0.230) (0.147) (0.230) (0.091) (0.232) (0.140) (0.327)LNETA 0.099*** 0.046 0.020 0.322 −0.017 0.285 −0.057 0.305 0.017 0.251 0.019 0.316

(0.024) (0.159) (0.112) (0.211) (0.113) (0.218) (0.131) (0.217) (0.099) (0.229) (0.122) (0.245)LNNIETA −0.127*** −0.350 −0.018 −0.357 0.040 −0.321 0.036 −0.326 −0.016 −0.310 −0.018 −0.353

(0.035) (0.336) (0.059) (0.354) (0.103) (0.375) (0.096) (0.380) (0.060) (0.356) (0.072) (0.361)LNLOANSTA 0.618** 0.941* 0.476 0.808 0.542 0.970 0.577 0.928 0.488 1.017 0.492 0.842

(0.238) (0.538) (0.413) (0.634) (0.423) (0.715) (0.445) (0.693) (0.465) (0.730) (0.421) (0.610)

Macroeconomic variablesLNGDP 0.898 −0.285 −0.062 −2.485 −0.204 −2.028 0.862 −0.483 0.925 −0.636

(0.782) (1.544) (0.848) (2.085) (0.872) (2.083) (0.869) (1.581) (0.769) (1.381)LNINFL 0.045 0.785*** 0.528** 1.721*** 0.578** 1.531*** 0.057 0.931*** 0.046 0.937**

(0.035) (0.109) (0.251) (0.470) (0.275) (0.505) (0.072) (0.224) (0.080) (0.414)LNCR3 0.411*** 1.538** 1.036** 2.904*** 1.135** 2.716*** 0.421*** 1.738*** 0.414** 1.725**

(0.131) (0.668) (0.405) (0.580) (0.446) (0.681) (0.114) (0.597) (0.182) (0.853)PTC 0.189* 0.408**

(0.106) (0.199)

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Bank-specific on post global financial crisis variablesLNTA*PTC 0.042* 0.069

(0.024) (0.045)LNLLRGL*PTC 0.012 0.170

(0.055) (0.164)LNNIITA*PTC −0.005 0.134

(0.078) (0.314)LNETA*PTCLNNIETA*PTCLNLOANSTA*PTC

Macroeconomic on post global financial crisis variablesLNGDP*PTCLNINFL*PTCLNCR3*PTC

R2 0.990 0.028 0.996 0.061 0.703 0.070 0.722 0.071 0.980 0.062 0.933 0.063Adj R2 0.987 −0.008 0.995 0.008 0.622 0.011 0.647 0.012 0.974 0.002 0.915 0.004F-statistic 414.962*** 0.784 918.943*** 1.153 8.677*** 1.192 9.538*** 1.205 176.852*** 1.040 51.373*** 1.061Durbin–Watson stat 2.248 1.956 2.266 1.920 2.307 1.927 2.317 1.884 2.267 1.934 2.274 1.909BP & LM x2 212.210*** 421.840*** 421.540*** 383.960*** 413.960*** 411.890***

Hausman x2 11.881 16.6 17.988 22.235** 15.869 18.359**

Sel Est method RE RE RE FE RE FENo. of Obs. 169 169 169 169 169 169 169 169 169 169 169 169

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C −3.249 −1.447 −4.194** −6.861 −2.822 −0.681 −2.650 −0.799 −3.609* −2.431 −2.672 −0.511(2.114) (4.696) (2.139) (6.037) (2.091) (5.498) (2.081) (5.573) (2.055) (5.381) (2.146) (5.593)

Bank-specific variablesLNTA −0.094 0.203*** −0.078 0.216*** −0.159 0.212*** −0.136 0.211*** −0.125 0.212*** −0.134 0.211***

(0.164) (0.063) (0.138) (0.063) (0.183) (0.066) (0.172) (0.065) (0.160) (0.065) (0.173) (0.065)LNLLRGL −0.038 0.041 −0.051 0.001 −0.027 0.043 −0.028 0.041 −0.036 0.038 −0.029 0.041

(0.029) (0.067) (0.038) (0.063) (0.034) (0.075) (0.034) (0.074) (0.033) (0.073) (0.034) (0.074)LNNIITA −0.142 −0.446* −0.084 −0.293 −0.130 −0.395* −0.131 −0.397* −0.108 −0.390* −0.129 −0.399*

(0.162) (0.238) (0.125) (0.250) (0.145) (0.231) (0.145) (0.230) (0.141) (0.233) (0.147) (0.231)LNETA 0.013 0.208 0.015 0.330 −0.018 0.293 −0.017 0.285 −0.009 0.291 −0.017 0.285

(0.107) (0.244) (0.111) (0.206) (0.116) (0.218) (0.113) (0.218) (0.109) (0.219) (0.113) (0.218)LNNIETA 0.047 −0.324 −0.023 −0.265 0.036 −0.321 0.040 −0.321 0.022 −0.330 0.037 −0.324

(0.112) (0.378) (0.054) (0.220) (0.105) (0.373) (0.103) (0.375) (0.092) (0.373) (0.102) (0.374)LNLOANSTA 0.529 0.920 0.496 0.685 0.496 0.873 0.541 0.968 0.500 0.907 0.529 0.969

(0.409) (0.651) (0.410) (0.594) (0.417) (0.686) (0.423) (0.714) (0.408) (0.685) (0.420) (0.713)

Macroeconomic variablesLNGDP 0.188 −2.220 0.731 0.620 0.061 −2.439 −0.051 −2.465 0.360 −1.837 0.012 −2.474

(0.828) (1.675) (0.894) (1.830) (0.836) (2.032) (0.846) (2.085) (0.823) (1.985) (0.862) (2.046)LNINFL 0.355* 1.619*** 0.087 0.455 0.518** 1.703*** 0.518** 1.703*** 0.257* 1.258*** 0.513** 1.749***

(0.197) (0.394) (0.095) (0.530) (0.252) (0.437) (0.247) (0.467) (0.143) (0.345) (0.255) (0.473)LNCR3 0.878** 2.968*** 0.460** 1.071 1.026** 2.889*** 1.040** 2.916*** 0.914** 2.896*** 0.934** 2.736***

(0.405) (0.739) (0.207) (1.044) (0.413) (0.574) (0.409) (0.591) (0.406) (0.826) (0.367) (0.545)PTC

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Bank-specific on post global financial crisis variablesLNTA*PTCLNLLRGL*PTCLNNIITA*PTCLNETA*PTC 0.135 0.428**

(0.095) (0.194)LNNIETA*PTC 0.039 −0.314

(0.118) (0.415)LNLOANSTA*PTC 0.100* 0.218**

(0.057) (0.103)

Macroeconomic on post global financial crisis variablesLNGDP*PTC 0.053* 0.114**

(0.030) (0.056)LNINFL*PTC 0.142 0.375

(0.099) (0.240)LNCR3*PTC 0.109* 0.250**

(0.064) (0.120)

R2 0.642 0.075 0.934 0.064 0.694 0.070 0.704 0.070 0.738 0.068 0.695 0.070Adj R2 0.545 0.017 0.916 0.004 0.610 0.011 0.623 0.011 0.667 0.009 0.611 0.011F-statistic 6.584*** 1.282 52.138*** 1.071 8.311*** 1.190 8.722*** 1.191 10.330*** 1.145 8.345*** 1.195Durbin–Watson stat 2.299 1.891 2.270 1.936 2.309 1.932 2.306 1.927 2.296 1.925 2.306 1.927BP & LM x2 418.300*** 418.040*** 422.320*** 421.550*** 421.930*** 421.520***

Hausman x2 20.808** 16.544 17.682 17.986 17.849 17.926Sel Est method FE RE RE RE RE RENo. of Obs. 169 169 169 169 169 169 169 169 169 169 169 169




LNNIITA*PTC, LNETA*PTC, LNNIETA*PTC, LNLOANSTA*PTC, LNGDP*PTC,LNINFL*PTC and LNCR3*PTC.

Tables 9 and 10 summarize that, size (LNTA) reveals a positive relationship with profit effi-ciency for both SCBs and PCBs and this is statistically significant at 1% and 5% levels (exceptModel 4 of Table 10). This result implies that the larger (smaller) size banks tend to exhibit higher(lower) profit efficiency. This provide support to the argument that large banks may benefit fromeconomies of scale which enables them to generate higher profits. Large banks may achieve higherprofit efficiency levels because their costs are compensated by higher profits that are generated viaquality services. Besides, large banks may have better capabilities to capitalize on profit enhance-ment activities and better cost cutting opportunities compared to their smaller bank counterparts.This result is consistent with study such as Sufian et al. (2012) providing support to the argumentthat big banks have high efficiency levels compared to medium and small banks.

The coefficient of LNNIITA has consistently exhibits a negative sign (statistically significantat the 5% level or better) for both banks (Tables 9 and 10). The results imply that banks whichderived a higher proportion of its income from non-interest sources such as fee based servicestend to be relatively less efficient in their intermediation function. The finding is in consonancewith the earlier studies by among others Sufian and Habibullah (2009b) and Stiroh (2006). Torecap, Stiroh and Rumble (2006) find that diversification benefits of the U.S. financial holdingcompanies are offset by the increased exposure to non-interest activities, which are much morevolatile, but not necessarily more profitable than interest generating activities.

With regard to the impact of capitalization (LNETA) on profit efficiency it can be observedfrom Tables 9 and 10 that the coefficient LNETA exhibits a positive sign for both SCBs andPCBs (except Model 1 of Table 9 and Models 5,6,7 of Table 10). The positive coefficient ofcapitalization signifies the positive relationship between capitalization and profit efficiency of thebanks where the well-capitalized banks would increase banks’ revenue and profitability due tothe lower expected costs of financial distress, lower expected bankruptcy costs, and lower riskof portfolio. Such advantages will then be translated into high profitability (Demirguc-Kunt &Huizinga, 1999).

Likewise, it can be observed from Table 9 that the overhead expenses (LNNIETA) and liquidity(LNLOANSTA) have significant positive relationship only in SCBs at the 1% level. The LNNIETAis the proxy of overhead expenses applied to provide the information on variation in operating costsacross the financial system. It reflects employment, total amount of wages and salaries, as well asthe cost of running branch office facilities. The positive sign indicates that higher profit earned bybanks that are more efficient may be appropriated in the form of higher payroll expenditures paidto the more productive human capital. This ensures a high SCBs profit efficiency in Bangladesh.

On the other hand, referring to the impact of bank’s loan intensity or liquidity, we findthat LNLOANSTA is positively related only to the profit efficiency of SCBs operating in theBangladesh banks. This finding implies that the higher the liquidity is, the higher the SCBs profitefficiency is. Higher liquidity is required to fund large loans in order to increase the profitabilityof the banks. The liquidity risk arises from the possible inability of banks to accommodate declin-ing liabilities or to provide funds on the assets’ side of the balance sheet. This is considered animportant determinant of the banks’ efficiency. Higher expected return is expected to be generatedfrom the risky loan market (bank’s asset). Thus, a higher liquidity is required to fund large loansin order to increase the profitability of the SCBs and this implies that liquidity has a positiverelationship with banks’ profit efficiency.

The findings suggest that the coefficient for economic growth (LNGDP) is negative for bothSCBs (refer Models 2,5,6,7,8 of Table 9) and PCBs (refer Models 3,4,7,9,10,11,12 of Table 10).


The negative sign indicates that the higher the economic growth is, the lower the bank profitefficiency is. This result may be due to the volatile economic growth that drives banks to sufferfrom lower demand for their financial services, increased loan defaults and thus depleted outputs.

This study discover that, the coefficient of LNINFL and lnCR3 are mixed (positive and negative)in SCBs but produce consistently positive sign in PCBs. On one hand,the LNINFL variable ispositive sign indicates the banks will charge a higher interest rate and obtain higher profit. Thebanks may obtain higher income since the number of borrowers increases even though the interestrate is higher because the consumer assumes there will be much higher inflation for the futurethat will lead to further increase in interest rates. On the other hands, the negative relationshipindicate that the borrowers will react negatively to the increase in inflation as they believe the rateof inflation may be reduced in the future.

Furthermore, the lnCR3 has a positive sign under both SCBs and PCBs providing support tothe Structure-Conduct-Performance (SCP) hypothesis. The SCP theory posits that the SCBs andPCBs banks in a highly concentrated market tend to collude, and therefore earn monopoly profits.The higher the bank concentration is, the lower the cost of collusion between firms or banksis. This will lead to higher bank profitability. Market concentration reveals the monopoly powerof the banks. This explains the positive relationship between market concentrations with bankefficiency. Meanwhile the coefficient of market concentration (lnCR3) also exhibits a negativesign for SCB banks (Table 9). The negative sign for market concentration in SCBs imply thatincreased bank concentration (by increasing the credit cost) will reduce the firms’ demand forcredit and consequently retard or slow down the growth of the economy.

Finally, it is discovered that the impact of global financial crisis or post global financial cri-sis (PTC) is significantly negative and positively to the profit efficiency of the SCBs and PCBs.The negative sign in Table 9 indicate that the SCBs banks’ funding structure depends mostly onwholesale funding that consists of funding from other nonbank investors, other banks, corpo-rate treasuries and money market funds. This wholesale funding that is practised by the banks ismore exposed to the liquidity risk that can be disseminated via the financial sector’s interlinkedrelationship which contributes the banks’ vulnerability to liquidity shocks. Therefore, the finan-cial crisis lead to liquidity shock that hitting one bank may lead to bank runs on solvent bankssince depositors may assume that the whole banking system is bogged with insufficient liquidity.Liquidity shock limits the banks capabilities to run their operation and lead to lower banks’ profitefficiency. It could also cause bankruptcies in the banking system (Smolo & Mirakhor, 2010).

Meanwhile, the coefficient of the PTC exhibits an positive relationship with the PCBs’ profitefficiency in Table 10. This indicates that global financial crisis may increase the banks’profit efficiency due to the excess liquidity in their accounts. Therefore, PCBs need to hold mas-sive cash or very low return assets as their liquid assets in order to avoid liquidity risk. Becauseof this reason, the PCBs are stable during the financial crisis in contrast to SCBs.

Determinants of profit efficiency on SCBs and PCBs specifically on post global financial crisis

The internal and external determinants of SCBs and PCBs profit efficiency may react differ-ently on post global financial crisis. In what precedes, we seek to identify factors which influencethe profit efficiency of the both banks on post global financial crisis. To do so, we include theinteractions of all the bank specific and macroeconomic determinants against the PTC variable. Asa result, 9 new bank specific interaction variables namely LNTA*PTC, LNLLRGL*PTC, LNNI-ITA*PTC, LNETA*PTC, LNNIETA*PTC, LNLOANSTA*PTC, LNGDP*PTC, LNINFL*PTCand LNCR3*PTC are introduced in regression Models 4 to 12 respectively.


The empirical findings presented in Model 4,7,11,12 of Table 10 suggest that the result ofLNTA*PTC, LNETA*PTC, LNINFL*PTC and LNCR3*PTC coefficient remain the same onPCBs but not SCBs. The LNTA*PTC, LNINFL*PTC, LNCR3*PTC variables changed the sign tonegative and it should be noted that the coefficient of the LNETA*PTC is statistically insignificantafter controlling the post global financial crisis period in SCBs (Table 9).

The size of banks (LNTA*PTC) turn negative to SCBs (Table 9). The empirical findingspresented in Model 4 of Tables 9 and 10 suggest that the sign of LNTA*PTC coefficient remainthe same on PCBs but turn negative to SCBs. The large SCBs operate at diseconomies of scalesince banks only enjoy small increase of output although the inputs are proportionately increased.Besides, large banks have the capabilities to increase the diversification to reduce the credit riskbut it would drive the returns lower. Indirectly, this indicates that the effect of large SCBs size canbe negative with the banks’ profitability on post global financial crisis

Furthermore, the LNLLRGL*PTC have a negatively significant only to the SCBs profitefficiency. This implies that SCBs with a higher credit risk will have lower bank efficiency.A higher credit risk means that banks may deal with a higher possibility that its loans will becomenon-performing. Thus, the higher credit risk indicates the higher possibility of the banks to beconfronted with unpaid loans and increasing of the non-performing loans, which both, implylower asset quality.

Turning to the liquidity variable, the empirical findings seem to suggest the LNLOANSTA*PTCturns into negative and significant with the SCBs profit efficiency but positively to PCBs aftercontrolling the post global financial crisis. The negative LNLOANSTA*PTC to the SCBs profitefficiency indicate that a high amount of liquidity could have an effect on the banks during a weakeconomy (global financial crisis). Borrowers are likely to default on their loans and this driveslower profitability.

A closer examination on the variable of economic growth on post global financial crisis indi-cates a changing sign to positive relationship with the profit efficiency of the PCBs (Model 11 ofTable 11) but remain the same on SCBs. The positive LNGDP*PTC signify that the favourableeconomic conditions during the global financial crisis period have fuelled higher demand for PCBsproducts and services, reducing default loan probabilities and thus increasing the profitability ofthe PCBs. High economic growth motivates PCBs to serve more loans and improve the qualityof their assets.

Main results: controlling for heteroscedasticity

In general, the preliminary results obtained using the OLS as an estimation model indi-cate that the coefficients of LNTA*PTC, LNLLRGL*PTC, LNLOANSTA*PTC, LNGDP*PTC,LNINFL*PTC and LNCR3*PTC represent as the determinants that negatively influence thelevel of SCBs profit efficiency specifically on post global financial crisis period (Table 9).Meanwhile, the coefficients of LNTA*PTC, LNETA*PTC, LNLOANSTA*PTC, LNGDP*PTC,LNINFL*PTC and LNCR3*PTC are significantly positive with the PCBs profit efficiency aftercontrolling the period of global financial crisis (Table 10). However, this study proceed the analy-sis using the GLS estimation method in order to obtain robust results. Therefore the main resultsare based on regression models under this estimation method.

The Generalized Least Square (GLS) comprising the Fixed Effect (FE) and Random Effect (RE)method is used for the robustness test rather than the Ordinary Least Square (OLS) as method ofestimation to estimate the panel data regression formed. The decision is made following Gujarati’s(2002) suggestion that GLS may overcome the heteroscedasticity, resulted from utilizing financial


data with differences in sizes. Due to the fact that the sample employed in this study consists ofsmall and large banks, differences in sizes of the observations are expected to be observed. Theusual practice of econometrics modelling assumes that error is constant over all time periods andlocations due to the existence of homoscedascity. Nevertheless, problems could arise which leadto heteroscedasticity issues as variance of the error term produced from regression tend not to beconstant, which is caused by variations of sizes in the observation. Therefore, the estimates of thedependent variable will be less predictable (Gujarati, 2002).

The results from Tables 11 and 12 shows that the panel data is most suitable to be used inthis study since the p-value of the Breusch Pagan and Lagrangian Multiplier (BP and LM) Chi-Square (χ2) test is significant at 5% and 1% levels in all models. Furthermore, the selection ofestimation method of FE (significant at 5% or better levels) and RE (insignificant) regressionanalysis are based on the Chi-Square (χ2) of the Hausman test. The results clearly indicate thatthe FE is preferable in Models (1,2,5,6,7,8,11 of Table 11 and 4,6,7 of Table 12) and RE issuitable in Models (3,4,9,10,12 of Table 11 and 1,2,3,5,8,9,10,11,12 of Table 12). Therefore, forthe purpose of this study, we will proceed with the analysis based on the FE and RE focusingonly on the specific determinants on post global financial crisis period (interaction model 4–12of Tables 11 and 12).

It can be observed from Models 4–12 of Tables 11 and 12 that the coefficients of the interactionvariables (specific determinants on post global financial crisis) mostly remain the same. Theyexhibit the same sign, the same order of magnitude, and remain significant as in the OLS regressionmodels (albeit sometimes at different levels). However, it can be observed from Table 11 thecoefficient of capitalization (LNETA*PTC) turn into significantly positive relationship with profitefficiency for SCBs (Model 7) under FE estimation after controlling for heteroscedasticity. Thisimplies, the higher capitalization, contribute to the higher SCBs’ profit efficiency specifically onpost global financial crisis.

Finally, it should be noted that the coefficient of the size (LNTA*PTC) is remain significantwith the PCBs profit efficiency under FE estimation (Model 4 of Table 12) when we controlfor potential heteroscedasticity. However, the result need be interpreted with caution since thecoefficient of the variable is significant only at 10% level. Meanwhile, when this study control forheteroscedasticity, the coefficient of inflation on post global financial crisis (LNINFL*PTC) turninto insignificant exhibited in Model 11 of Table 12. Thus, this explain that the factor of inflationis not significantly influence the profit efficiency of PCBs on post global financial crisis.

Conclusions

To date, studies on bank efficiency are numerous. However, most of these studies have con-centrated on the banking sectors of the western and developed countries. Therefore, this studyinvestigate for the first time empirical evidence on impact of global financial crisis focusing onSCBs and PCBs ownership and others bank specific and macroeconomics factors influencingprofit efficiency level of the Bangladesh banking sector. By using data on Bangladesh banks dur-ing the years 2004–2011 the present study fills in this demanding gap by providing new empiricalevidence. The present study consists of two stages. In the first stage, this study employ the Slack-Based Data Envelopment Analysis (SBM-DEA) method to measure the level of profit efficiencyon SCBs and PCBs over the period of pre, during and post global financial crisis. In the secondstage, this study adopt a multivariate panel regression analysis framework based on the OrdinaryLeast Square (OLS) and Generalized Least Square (GLS) methods comprising the Fixed Effect


(FE) and Random Effect (RE) models to examine the potential determinants of banks’ profitefficiency.

The empirical findings from the first stage indicate that the levels of profit efficiency on bothSCBs and PCBs are increasing by 3.7% (95.1% to 98.8%) on SCBs and increasing by 5.8%(71.9% to 77.7%) on PCBs during financial crisis years (2007–2008). However, over the periodof post financial crisis years (2009–2011) exhibited that, profit efficiency levels on SCBs andPCBs are decreasing by 38.7% (98.8% to 60.1%) on SCBs and decreasing by 9.9% (77.7% to67.8%) on PCBs.

Although profit efficiency levels on both ownership of banks shows declining over the postfinancial crisis years, the PCBs still higher than SCBs (67.8% > 60.1%) but insignificantly differ-ent. Accordingly, this results could suggest that to avoid the declining of the profit efficiency levelsover the period of post financial crisis, banks should identify the potential external and internaldeterminants that can significantly influence to the improvement of SCBs and PCBs efficiency.

Furthermore, the results from the multivariate panel regression analysis discovered the poten-tial bank specific and macroeconomics determinants that significantly influence the improvementof profit efficiency levels on both SCBs and PCBs specifically on post global financial cri-sis period. A closer examination on the findings reveals that the relationship of size of bank(LNTA*PTC), liquidity (LNLOANSTA*PTC), economic growth (LNGDP*PTC) and marketconcentration (LNCR3*PTC) are significantly negative with profit efficiency of SCBs but have apositive impact in the case of the PCBs ownership.

This result implies that over the period of post global financial crisis, the large size of banks,higher liquidity, well economic growth and higher market concentration tend to lead to lowerlevel of SCBs’ profit efficiency. This indicate that the SCBs operated at diseconomies of scale,borrowers are likely to default on their loans, the volatile of economic growth and reducing thecredit demand from the firms. Therefore, the results suggest that to improve or increase the levelof SCBs’ profit efficiency, banks should shrink the banks’ size, reduce the liquidity, await forthe stable economic growth and squeeze the market concentration since those determinants havenegative relationship with efficiency.

Meanwhile, the contra finding for the PCBs indicate that over the global financial crisis periodbanks may benefit from economies of scale, banks used huge liquidity to fund large loans, reducingon the default loan and the PCBs earn the monopoly profits. Thus, based on the findings reveal,the PCBs should promote growth for size of banks, enhance the liquidity, offer more loans andincrease the market concentration in order to obtain higher level of profit efficiency since thesevariables have a positive relationship with the banks’ profit efficiency specifically over the globalfinancial crisis period.

Furthermore, the variables of credit risk (LNLLRGL*PTC) and inflation (LNINFL*PTC) aresignificant with the negative sign but the capitalization (LNETA*PTC) is significantly positivewith the profit efficiency of the SCBs specifically on post global financial crisis period. This resultsindicate that the over the period of post crisis, SCBs with a higher credit risk (LNLLRGL*PTC)will have lower bank efficiency. A higher credit risk means that banks may deal with a higherpossibility that its loans will become non-performing. Thus, the higher credit risk indicates thehigher possibility of the banks to be confronted with unpaid loans and increasing of the non-performing loans, which both, imply lower asset quality. Furthermore, the negative coefficientof LNINFL*PTC indicate that the borrowers will react negatively to the increase in inflation asthey believe the rate of inflation may be reduced in the future. Finally, the positive coefficient ofcapitalization signifies the well-capitalized banks would increase banks’ revenue and profitability


due to the lower expected costs of financial distress, lower expected bankruptcy costs, and lowerrisk of portfolio.

The empirical findings from this study clearly call for regulators and decision makers to reviewthe profit efficiency of banks operating in the Bangladesh banking sector. This consideration isvital because profit efficiency is the most important concept which could lead to higher or lowerprofitability of the Bangladesh banking sector. The results could also provide better informationand guidance to bank managers, as they need to have clear understanding on the impact of profitefficiency on the performance of their banks. The findings may also have implications for investorswhose main goal is to reap higher profit from their investments. By doing so, they could concentrateon the potential profitability of banks before investing.

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Global financial crisis, ownership and bank profit ...Profit efficiency measures how close a bank is in producing the maximum level of profit, given the amount of inputs and outputs

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